Acute Myocardial Infarction Clinical Intelligent Decision Support System

Acute Myocardial Infarction (AMI) remains the leading cause of cardiovascular mortality globally. In China, while the incidence of AMI is escalating at an annual rate of 5.2%, significant clinical challenges persist: diagnostic delays in primary care facilities exceed 40%, and the "Door-to-Balloon" (D2B) compliance rate in tertiary hospitals stagnates at a mere 65%. These figures underscore systemic deficiencies, including inefficient emergency response, regional resource disparities, and fragmented longitudinal care. Although Large Language Models (LLMs) provide a transformative technical foundation for AMI management, their clinical translation is hindered by critical bottlenecks, such as non-standardized data interfaces, limited model interpretability, inadequate hardware infrastructure at the grassroots level, and the inherent tension between data privacy and training requirements.

This research proposes a comprehensive implementation strategy for an AI-driven intelligent decision-making system for AMI. On a theoretical level, the study establishes a tripartite framework of "Technological Adaptation, Scenario Implementation, and Safeguard Mechanisms." By introducing a data governance scheme based on federated learning and multimodal fusion, and constructing a "Technical-Clinical-Economic" multidimensional evaluation model, this work bridges the theoretical divide between advanced technology and clinical practice. On a practical level, the study develops adaptive gateways and lightweight models to facilitate pervasive deployment in resource-constrained settings, optimizes the full-cycle clinical workflow to improve patient outcomes, and provides a scalable, replicable pathway for implementation.

Focusing on four core challenges-technological compatibility, clinical workflow integration, the balance between privacy and performance, and the establishment of scientific evaluation systems-this research aims to surmount existing translation barriers. It seeks to enhance the quality and efficiency of AMI care while providing a seminal reference for the clinical transformation of AI in other medical specialties.